scholarly journals An adaptive algorithm for fast and reliable online saccade detection

2019 ◽  
Author(s):  
Richard Schweitzer ◽  
Martin Rolfs
Keyword(s):  
High Accuracy ◽  
False Alarms ◽  
Detection Accuracy ◽  
Display Change ◽  
Saccade Onset ◽  
High Detection ◽  
Saccade Direction ◽  
Spatial Boundary ◽  
Speed And Accuracy ◽  
Standard Techniques

AbstractTo investigate visual perception around the time of eye movements, vision scientists manipulate stimuli contingent upon the onset of a saccade. For these experimental paradigms, timing is especially crucial, as saccade offset imposes a deadline on the display change. Although efficient online saccade detection can greatly improve timing, most algorithms rely on spatial-boundary techniques or absolute-velocity thresholds, which both suffer from their respective weaknesses: late detections and false alarms. We propose an adaptive, velocity-based algorithm for online saccade detection that surpasses both standard techniques in speed and accuracy and allows the user to freely define detection criteria. Inspired by the Engbert-Kliegl-algorithm for microsaccade detection, our algorithm computes two-dimensional velocity thresholds from variance in preceding fixation samples, while compensating for noisy or missing data samples. An optional direction criterion limits detection to the instructed saccade direction, further increasing robustness. We validated the algorithm by simulating its performance on a large saccade dataset and found that high detection accuracy (false-alarm rates of <1%) could be achieved with detection latencies of only 3 milliseconds. High accuracy was maintained even under simulated high-noise conditions. To demonstrate that purely intra-saccadic presentations are technically feasible, we devised an experimental test, in which a Gabor patch drifted at saccadic peak velocities. While this stimulus was invisible when presented during fixation, observers reliably detected it during saccades. Photodiode measurements verified that – including all system delays – stimuli were physically displayed on average 20 ms after saccade onset. Thus, the proposed algorithm provides valuable tool for gaze-contingent paradigms.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

Perisaccadic Mislocalization of Visual Targets by Head-Free Gaze Shifts: Visual or Motor?

2008 ◽  
Vol 100 (4) ◽  
pp. 1848-1867 ◽  
Author(s):  
Sigrid M. C. I. van Wetter ◽  
A. John van Opstal
Keyword(s):  
Target Location ◽  
Saccadic Eye Movements ◽  
Open Loop ◽  
Spatial Updating ◽  
Gaze Shifts ◽  
Gaze Shift ◽  
The Gaze ◽  
Visual Probe ◽  
Saccade Onset ◽  
Saccade Direction

Such perisaccadic mislocalization is maximal in the direction of the saccade and varies systematically with the target-saccade onset delay. We have recently shown that under head-fixed conditions perisaccadic errors do not follow the quantitative predictions of current visuomotor models that explain these mislocalizations in terms of spatial updating. These models all assume sluggish eye-movement feedback and therefore predict that errors should vary systematically with the amplitude and kinematics of the intervening saccade. Instead, we reported that errors depend only weakly on the saccade amplitude. An alternative explanation for the data is that around the saccade the perceived target location undergoes a uniform transient shift in the saccade direction, but that the oculomotor feedback is, on average, accurate. This “ visual shift” hypothesis predicts that errors will also remain insensitive to kinematic variability within much larger head-free gaze shifts. Here we test this prediction by presenting a brief visual probe near the onset of gaze saccades between 40 and 70° amplitude. According to models with inaccurate gaze-motor feedback, the expected perisaccadic errors for such gaze shifts should be as large as 30° and depend heavily on the kinematics of the gaze shift. In contrast, we found that the actual peak errors were similar to those reported for much smaller saccadic eye movements, i.e., on average about 10°, and that neither gaze-shift amplitude nor kinematics plays a systematic role. Our data further corroborate the visual origin of perisaccadic mislocalization under open-loop conditions and strengthen the idea that efferent feedback signals in the gaze-control system are fast and accurate.

A New Approach to Pipeline Leak Detection Using Electromagnetic Sensing

2016 ◽  
Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell
Keyword(s):  
Leak Detection ◽  
False Alarms ◽  
Detection Accuracy ◽  
Minimal Risk ◽  
Detection Systems ◽  
Acoustic Signatures ◽  
Cost System ◽  
Trade Offs ◽  
In Situ Sensors ◽  
Operational Range

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).

scholarly journals Towards the Low False Alarms and High Detection Rate in Intrusions Detection System

2013 ◽  
pp. 332-336
Author(s):  
Hafiz Muhammad Imran ◽  
Azween Bin Abdullah ◽  
Sellappan Palaniappan
Keyword(s):  
Detection Rate ◽  
Detection System ◽  
False Alarms ◽  
High Detection Rate ◽  
High Detection

scholarly journals Concurrent Plantar Stress Sensing and Energy Harvesting Technique by Piezoelectric Insole Device and Rectifying Circuitry for Gait Monitoring in the Internet of Health Things

2020 ◽  
Author(s):  
Shuaibo Kang ◽  
Jingjing Lin ◽  
Junliang Chen ◽  
Yanning Dai ◽  
Zhiheng Wang ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Detection Sensitivity ◽  
Detection Accuracy ◽  
Stress Detection ◽  
Force Detection ◽  
Battery Lifetime ◽  
Harvesting Technique ◽  
High Detection ◽  
Stress Sensing ◽  
Gait Monitoring

Concurrent high force detection accuracy and extended battery lifetime are expected for wearable gait monitoring systems. In this article, a piezoelectric insole device and rectifying circuitry-based technique is presented to achieve these two goals. Here, walking induced positive and negative charges are separated for plantar stress detection and energy harvesting respectively, realizing the two functions concurrently. The high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases. 1.6 pJ is stored during a walking event, extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

PFM (piezoresponse force microscopy)-aided design for molecular ferroelectrics

2021 ◽  
Author(s):  
Han-Yue Zhang ◽  
Xiao-Gang Chen ◽  
Yuan-Yuan Tang ◽  
Wei-Qiang Liao ◽  
Fang-Fang Di ◽  
...  
Keyword(s):  
Rapid Development ◽  
Piezoresponse Force Microscopy ◽  
Force Microscopy ◽  
High Detection ◽  
Aided Design ◽  
Speed And Accuracy ◽  
Detection Speed

Along with the rapid development of ferroelectrochemistry, piezoresponse force microscopy (PFM) with high detection speed and accuracy has become a powerful tool for screening the potential candidates for molecular ferroelectrics.

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

2021 ◽  
pp. 1-14
Author(s):  
Hanqing Hu ◽  
Mehmed Kantardzic
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
False Alarms ◽  
Detection Accuracy ◽  
Real World Data ◽  
Traditional Concept ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Detection Algorithms ◽  
Concept Drift Detection

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

scholarly journals Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

2019 ◽  
Vol 11 (12) ◽  
pp. 3261 ◽  
Author(s):  
Jesus Olivares-Mercado ◽  
Karina Toscano-Medina ◽  
Gabriel Sánchez-Perez ◽  
Aldo Hernandez-Suarez ◽  
Hector Perez-Meana ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Fire Detection ◽  
Candidate Region ◽  
Support Vector ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multi Layer Perceptron ◽  
System Efficiency ◽  
Dynamic Texture ◽  
Early Fire Detection

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.

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